Search Results (1,413)

A standalone hybrid renewable energy system (HRES) that combines different types of renewable energy sources and storages offers a sustainable energy solution by reducing reliance on fossil fuels and minimizing greenhouse gas emissions. In this paper, a standalone hybrid renewable energy system (HRES) involving wind turbines, photovoltaic (PV) modules, diesel generators (DG), and battery banks is proposed. For this purpose, it is necessary to size and run the proposed system for feeding a residential load satisfactorily. For two typical winter and summer weeks, weather historical data, including irradiance, temperature, wind speed, and load profiles, are used as input data. The overall optimization framework is formulated as a bi-level mixed-integer nonlinear programming (BMINLP) problem. The upper-level part represents the sizing sub-problem that is solved based on economic and environmental multi-objectives. The lower-level part represents the energy management strategy (EMS) sub-problem. The EMS task utilizes the model predictive control (MPC) approach to achieve optimal technoeconomic operational performance. By the definition of BMINLP, the EMS sub-problem is defined within the constraints of the sizing sub-problem. The MATLAB R2023a environment is employed to execute and extract the results of the entire problem. The global optimization solver “ga” is utilized to implement the upper sub-problem while the “intlinprg” solver solves the lower sub-problem. The evaluation metrics used in this study are the operating, maintenance, and investment costs, storage unit degradation, and the number of CO₂ emissions. Full article

Accurate wind speed and direction data are vital for coastal engineering, renewable energy, and climate resilience, particularly in regions with sparse observational datasets. This study evaluates the ERA5 reanalysis model’s performance in predicting wind speeds and directions at ten coastal and offshore stations in Kuwait from 2010 to 2017. This analysis reveals that ERA5 effectively captures general wind speed patterns, with offshore stations demonstrating stronger correlations (up to 0.85) and higher Perkins Skill Score (PSS) values (up to 0.94). However, the model consistently underestimates wind variability and extreme wind events, especially at coastal stations, where correlation coefficients dropped to 0.35. Wind direction analysis highlighted ERA5’s ability to replicate dominant northwest wind patterns. However, it reveals notable biases and underrepresented variability during transitional seasons. Taylor diagrams and error metrics further emphasize ERA5’s challenges in capturing localized dynamics influenced by land-sea interactions. Enhancements such as localized calibration using high-resolution datasets, hybrid models incorporating machine learning techniques, and long-term monitoring networks are recommended to improve accuracy. By addressing these limitations, ERA5 can more effectively support engineering applications, including coastal infrastructure design and renewable energy development, while advancing Kuwait’s sustainable development goals. This study provides valuable insights into refining reanalysis model performance in complex coastal environments. Full article

As extreme forms of tropical cyclones (TCs), typhoons pose significant threats to both human society and the natural environment. To better understand and predict their behavior, scientists have relied on numerical simulations. Current typhoon modeling primarily falls into two categories: (1) complex simulations based on fluid dynamics and thermodynamics, and (2) empirical parameterized models. Most comparative studies on these models have focused on wind speed below 50 m/s, with fewer studies addressing high wind speed (above 50 m/s). In this study, we design and compare four different simulation approaches to model two super typhoons: Typhoon Surigae (2102) and Typhoon Nepartak (1601). These approaches include: (1) The Weather Research and Forecasting (WRF) model simulation driven by NCEP Final Operational Global Analysis data (FNL), (2) WRF simulation driven by the fifth generation of the European Centre for Medium-Range Weather Forecasts (ECMWF) reanalysis data (ERA5), (3) the empirical parameterized Holland model, and (4) the empirical parameterized Jelesnianski model. The simulated wind fields were compared with the measured wind data from The Soil Moisture Active Passive (SMAP) platform, and the resulting wind fields were then used as inputs for the Simulating WAves Nearshore (SWAN) model to simulate typhoon-induced waves. Our findings are as follows: (1) for high wind speeds, the performance of the empirical models surpasses that of the WRF simulations; (2) using more accurate driving wind data improves the WRF model’s performance in simulating typhoon wind speeds, and WRF simulations excel in representing wind fields in the outer regions of the typhoon; (3) careful adjustment of the maximum wind speed radius parameter is essential for improving the accuracy of the empirical models. Full article

Extremely severe cyclonic storms over the North Indian Ocean increased by approximately 10% during the past 30 years. The climatological characteristics of tropical cyclones for 38 years were assessed over the Bay of Bengal (BoB). A total of 24 ESCSs formed over the BoB, having their genesis in the southeast BoB, and the intensity and duration of these storms have increased in recent times. The Advanced Research version of the Weather Research and Forecasting (ARW) model is utilized to simulate the five extremely severe cyclonic storms (ESCSs) over the BoB during the past two decades using the Indian Monsoon Data Assimilation and Analysis (IMDAA) data. The initial and lateral boundary conditions are derived from the IMDAA datasets with a horizontal resolution of 0.12° × 0.12°. Five ESCSs from the past two decades were considered: Sidr 2007, Phailin 2013, Hudhud 2014, Fani 2019, and Amphan 2020. The model was integrated up to 96 h using double-nested domains of 12 km and 4 km. Model performance was evaluated using the 4 km results, compared with the available observational datasets, including the best-fit data from the India Meteorological Department (IMD), the Tropical Rainfall Measuring Mission (TRMM) satellite, and the Doppler Weather Radar (DWR). The results indicated that IMDAA provided accurate forecasts for Fani, Hudhud, and Phailin regarding the track, intensity, and mean sea level pressure, aligning well with the IMD observational datasets. Statistical evaluation was performed to estimate the model skills using Mean Absolute Error (MAE), the Root Mean Square Error (RMSE), the Probability of Detection (POD), the Brier Score, and the Critical Successive Index (CSI). The calculated mean absolute maximum sustained wind speed errors ranged from 8.4 m/s to 10.6 m/s from day 1 to day 4, while mean track errors ranged from 100 km to 496 km for a day. The results highlighted the prediction of rainfall, maximum reflectivity, and the associated structure of the storms. The predicted 24 h accumulated rainfall is well captured by the model with a high POD (96% for the range of 35.6–64.4 mm/day) and a good correlation (65–97%) for the majority of storms. Similarly, the Brier Score showed a value of 0.01, indicating the high performance of the model forecast for maximum surface winds. The Critical Successive Index was 0.6, indicating the moderate model performance in the prediction of tracks. It is evident from the statistical analysis that the performance of the model is good in forecasting storm structure, intensity and rainfall. However, the IMDAA data have certain limitations in predicting the tracks due to inadequate representation of the large-scale circulations, necessitating improvement. Full article

As a potential consequence of climate change, the intensity and frequency of dust storms are increasing. A dust storm arises when strong winds blow loose dust from a dry surface, transporting soil particles from one place to another. The environmental and human health impacts of dust storms are substantial. Accordingly, studying the monitoring of this phenomenon and predicting its pathways for early decision making and warning are vital. This study employs deep learning methods to predict dust storm pathways. Specifically, hybrid CNN-LSTM and ConvLSTM models have been proposed for the 24 h-ahead prediction of dust storms in the region under study. The Modern-Era Retrospective Analysis for Research and Applications version 2 (MERRA-2) product that includes the dust particles and the meteorological information, such as surface wind speed and direction, relative humidity, surface air temperature, and skin temperature, is used to train the proposed models. These contextual features are selected utilizing the random forest feature importance method. The results indicate an improvement in the performance of both models by considering the contextual information. Moreover, a 0.2 increase in the Kappa coefficient criterion across all forecast hours indicates the CNN-LSTM model outperforms the ConvLSTM model when contextual information is considered. Full article

Forest fires pose a significant ecological threat, particularly in the Diamer District, Gilgit-Baltistan, Pakistan, where climatic factors combined with human activities have resulted in severe fire incidents. The present study sought to investigate the correlation between the incidence of forest fires and critical meteorological elements, including temperature, humidity, precipitation, and wind speed, over a period of 25 years, from 1998 to 2023. We analyzed 169 recorded fire events, collectively burning approximately 109,400 hectares of forest land. Employing sophisticated machine learning algorithms, Random Forest (RF), and Gradient Boosting Machine (GBM) revealed that temperature and relative humidity during the critical fire season, which spans May through July, are key factors influencing fire activity. Conversely, wind speed was found to have a negligible impact. The RF model demonstrated superior predictive accuracy compared to the GBM model, achieving an RMSE of 5803.69 and accounting for 49.47% of the variance in the burned area. This study presents a novel methodology for predictive fire risk modeling under climate change scenarios in the region, offering significant insights into fire management strategies. Our results underscore the necessity for real-time early warning systems and adaptive management strategies to mitigate the frequency and intensity of escalating forest fires driven by climate change. Full article

As an emerging rapid manufacturing technology, 3D printing has been widely applied in numerous fields such as aerospace, shipbuilding, and wind power, by virtue of its advantage in efficiently fabricating components with complex structures and integrated functions. In response to the problems of poor mechanical properties and difficulty in selecting process parameters for fused deposition modeling (FDM), this paper analyzed the principle of FDM and proposed a parameter optimization method based on a Kriging and Cuckoo Search (CS) algorithm aimed at improving the mechanical properties of 3D printed polylactic acid (PLA) parts. Firstly, by analyzing FDM principle and its main parameters, printing speed and temperature were selected as research elements, and tensile strength as the mechanical performance index. Latin hypercube sampling (LHS) was integrated to generate a limited experimental sample set. Secondly, a Kriging-based prediction model for mechanical properties was constructed by learning sample data, and the nonlinear mapping relationship between process parameters and tensile strength was obtained. Then, using the combinations of speed and temperature as design variables and maximizing tensile strength as the optimization objective, an optimization model was established, and the optimal process parameters were searched by CS. The optimal printing velocity was 31 mm/s and printing temperature was 225 °C, and the corresponding maximum tensile strength was 38.27 MPa. Finally, compared to the test data, the relative prediction error of Kriging model was 0.62%, and the optimal strength (38.27 MPa) increased by about 12.7% compared to the average value (33.97 MPa) of experimental data. It can be seen that the Kriging model is effective, and the tensile strength of parts printed under the optimal process parameters is significantly improved. Full article

Wind speed forecasting is an essential part of weather prediction, with significant value in economics, business, and management. Utilizing multiple meteorological variables can improve prediction accuracy, but existing methods face challenges such as mixing and noise due to variable differences, as well as difficulty in capturing complex spatio-temporal dependencies. To address these issues, this study introduces a novel short-term wind speed forecasting model named as MIESTC. The proposed model employs an independent encoder to extract features from each meteorological variable, mitigating the issues of noise that are caused by variable mixing. Then, a multivariate spatio-temporal correlation module is used to capture the global spatio-temporal dependencies between variables and model their interactions. Experimental results on the ERA5-LAND dataset show that, compared to the ConvLSTM, UNET, and SimVP models, the MIESTC model reduces RMSE by 14.60%, 8.64%, and 10.41%, respectively, for a 1 h prediction duration. For a 6 h prediction duration, the corresponding reductions are 13.91%, 8.20%, and 6.95%, validating its superior performance in short-term wind speed forecasting. Furthermore, an analysis of variable impacts reveals that U10, V10, and T2M play dominant roles in wind speed prediction, while TP exhibits a relatively lower impact, aligning with the results of the correlation analysis. These findings underscore the potential of MIESTC as an effective and reliable tool for short-term wind speed prediction. Full article

Pangu is an AI-based model designed for rapid and accurate numerical weather forecasting. To evaluate Pangu’s short- to medium-term weather forecasting skill over various meteorological parameters, this paper validated its performance in predicting temperature, wind speed, wind direction, and barometric pressure using data from over 2000 weather stations in China. Pangu’s performance was compared with ECMWF-HRES and GFS to assess its effectiveness relative to traditional high-precision NWP models under real meteorological conditions. Furthermore, the more recent FuXi and FengWu models were included in the analysis to further validate Pangu’s forecasting skill. The study examined Pangu’s forecast performance from spatial perspectives, evaluated the dispersion of forecast deviations, and analyzed its performance at different lead times and with various initial fields. The iteration precision of Pangu’s four forecast models with lead times of 1 h, 3 h, 6 h, and 24 h was also assessed. Finally, a case study on typhoon track forecasting was conducted to evaluate Pangu’s performance in predicting typhoon paths. The results indicate that Pangu surpasses traditional NWP systems in temperature forecasting, while its performance in predicting wind direction, wind speed and pressure is comparable to them. Additionally, the forecast skill of Pangu diminishes as the lead time extends, but it tends to surpass traditional NWP systems with longer lead times. Moreover, FuXi and FengWu demonstrate even higher accuracy compared to Pangu. Pangu’s performance is also dependent on initial fields, and the temperature forecasting of Pangu is more sensitive to the initial field compared with other meteorological parameters. Furthermore, the iteration precision of Pangu’s 1 h forecast model is significantly lower than that of the other models, but this discrepancy in precision may not be prominently reflected in Pangu’s actual forecasting process due to the greedy algorithm employed. In the case study on typhoon forecasting, Pangu, along with FuXi and FengWu, demonstrates comparable performance in predicting Bebinca’s track compared to ECMWF and outperforms GFS in its track predictions. This study demonstrated Pangu’s applicability in short- to medium-term forecasting of meteorological parameters, showcasing the significant potential of AI-based numerical weather models in enhancing forecast performance. Full article

The effects of an increase in output power owing to the close arrangement of vertical-axis wind turbines (VAWTs) are well known. With the ultimate goal of determining the optimal layout of a wind farm (WF) for VAWTs, this study proposes a new method for quickly calculating the flow field and power output of a virtual WF consisting of two-dimensional (2-D) miniature VAWT rotors. This new method constructs a flow field in a WF by superposing 2-D velocity numerical data around an isolated single VAWT obtained through a computational fluid dynamics (CFD) analysis. In the calculation process, the VAWTs were gradually increased one by one from the upstream side, and a calculation subroutine, in which the virtual upstream wind speed at each VAWT position was recalculated with the effects of other VAWTs, was repeated three times for each arrangement with a temporal number of VAWTs. This method includes the effects of the velocity gradient, secondary flow, and wake shift as models of turbine-to-turbine interaction. To verify the accuracy of the method, the VAWT rotor power outputs predicted by the proposed method for several types of rotor pairs, four-rotor tandem, and parallel arrangements were compared with the results of previous CFD analyses. This method was applied to four virtual WFs consisting of 16 miniature VAWTs. It was found that a layout consisting of two linear arrays of eight closely spaced VAWTs with wide spacing between the arrays yielded a significantly higher output than the other three layouts. The high-performance layout had fewer rotors in the wakes of the other rotors, and the induced flow speeds generated by the closely spaced VAWTs probably mutually enhanced their output power. Full article

To enhance the prediction accuracy of offshore wind speed, this study employs an interpolation algorithm to improve spatial resolution based on the ERA5 reanalysis dataset. The objective is to identify the optimal interpolation method and apply it to wind energy assessments in the South China Sea. This paper compares the interpolation effects and accuracy of Linear, Cubic, and Bicubic interpolation methods on wind speed data, with the optimal method subsequently applied to evaluate wind resources in the South China Sea for 2023. The findings indicate that, while different interpolation methods minimally affect the correlation of wind speed data, there are notable differences in their impact on overall accuracy. The Cubic interpolation method proved to be the most effective, tripling spatial resolution and reducing wind speed errors in ERA5 data by 26%. Using this method, wind resource assessments were conducted in selected areas of the South China Sea. Results reveal that the annual available operational hours for wind turbines in most parts of the region range from 2000 to 4000 h, with fluctuations in turbine output power increasing alongside available operational hours. Full article

The formation of forest fire burned area, influenced by a variety of factors such as meteorology, topography, vegetation, and human intervention, is a dynamic process of fire line burning that develops from the point of ignition to the boundary of the burned area. Accurately simulating and predicting this dynamic process can provide a scientific basis for forest fire control and suppression decisions. In this study, five typical forest fires located in different regions of China were used as the study object. The straight path distances from the ignition point grid to each grid on fire line in Sentinel-2 imageries for each forest fire were used as the target variables. We obtained the values of 11 independent variables for each pathway, including wind speed component, Temperature, Relative Humidity, Elevation, Slope, Aspect, Degree of Relief, Normalized Difference Vegetation Index, Vegetation Type, Fire Duration, and Gross Domestic Product reflecting human intervention capacity for fires. The value of each target variable and that of its corresponding independent variable constituted a sample. Four machine learning models, such as Random Forest (RF), Gradient Boosting Decision Trees (GBDT), Support Vector Machine (SVM), and Multilayer Perceptron (MLP), were trained using 80% effective samples from four forest fires, and 20% used to verify the above models. The hyper-parameters of each model were optimized using grid search method. After analyzing the validation results of models which showed temperature as a non-significant variable, the training and validation process of models above was repeated after excluding temperature. The results show that RF is the optimal model with 49.55 m for root mean square error (RMSE), 29.19 m for mean absolute error (MAE) and 0.9823 for coefficient of determination (R²). This study used the RF model to construct the shape of burned areas by predicting lengths of all straight path distances from the ignition point to the fire line. The study can dynamically capture the development of forest fire scenes. Full article

Wind energy has emerged as a viable alternative to fossil fuels due to its clean and cost-effective nature. Pakistan, facing growing energy demands and the imperative to reduce carbon emissions, has invested significantly in wind power to supply electric power in rural and urban communities, particularly in the Thatta district of Sindh Province of Pakistan. However, the sustainability of wind energy generation is contingent upon consistent and sufficient wind resources. This study examines the wind potential of Thatta district from 2004 to 2023 to assess its suitability for large-scale wind power development. To evaluate the wind potential of Thatta district, seasonal wind speed and direction data were collected and analyzed. Wind shear at different heights was determined using the power law, and wind potential maps were generated using GIS interpolation techniques. Betz’s law was employed to assess wind turbine power density. Box–Jenkins ARIMA and SARIMA models were applied to predict future wind patterns. This study revealed that Thatta district experienced sufficient wind speeds during the study period, with averages of 9.7 m/s, 7.6 m/s, 7.4 m/s, and 4.8 m/s for summer, autumn, spring, and winter, respectively. However, a concerning trend of decreasing wind speeds has been observed since 2009. The most significant reductions occurred in summer, coinciding with Pakistan’s peak electricity demand. While Thatta district has historically demonstrated potential for wind energy, the declining wind speeds pose a challenge to the sustainability of wind power projects. Further research is necessary to identify the causes of this trend and to explore mitigation strategies. Full article

Since private cars and vans accounted for more than 25% of global oil consumption and about 10% of energy-related CO₂ emissions in 2022, increasing the share of electric vehicle (EV) ownership is considered an important solution for reducing CO₂ emissions. At the same time, reducing emissions entails certain economic losses for those countries whose exports are largely covered by the oil trade. The explosive growth of the EV segment over the past 15 years has given rise to overly optimistic forecasts for global EV penetration by 2050. One of the major obstacles to such a development scenario is the limited availability of resources, especially critical materials. This paper proposes a mathematical model to predict the global EV fleet based on the limited availability of critical materials such as lithium, one of the key elements for battery production. The proposed model has three distinctive features. First, it shows that the classical logistic function, due to the specificity of its structure, cannot correctly describe market saturation in the case of using resources with limited serves. Second, even the use of a special multiplier that describes the market saturation process taking into account the depletion (finiteness) of the used resource does not obtain satisfactory economic results because of the “high speed” depletion of this resource. Third, the analytical solution of the final model indicates the point in time at which changes in saturation rate occur. The latter situation allows us to determine the tracking of market saturation, which is more similar to the process that is actually occurring. We believe that this model can also be validated to estimate the production of wind turbines that use rare earth elements such as neodymium and dysprosium (for the production of powerful and permanent magnets for wind turbines). These results also suggest the need for oil-exporting countries to technologically diversify their economies to minimize losses in the transition to a low-carbon economy. Full article

DC micro-grids are emerging as a promising solution for efficiently integrating renewable energy into power systems. These systems offer increased flexibility and enhanced energy management, making them ideal for applications such as heat pump (HP) systems. However, the integration of intermittent renewable energy sources with optimal energy management in these micro-grids poses significant challenges. This paper proposes a novel control strategy designed specifically to improve the performance of DC micro-grids. The strategy enhances energy management by leveraging an environmental mission profile that includes real-time measurements for energy generation and heat pump performance evaluation. This micro-grid application for heat pumps integrates photovoltaic (PV) systems, wind generators (WGs), DC-DC converters, and battery energy storage (BS) systems. The proposed control strategy employs an intelligent maximum power point tracking (MPPT) approach that uses optimization algorithms to finely adjust interactions among the subsystems, including renewable energy sources, storage batteries, and the load (heat pump). The main objective of this strategy is to maximize energy production, improve system stability, and reduce operating costs. To achieve this, it considers factors such as heating and cooling demand, power fluctuations from renewable sources, and the MPPT requirements of the PV system. Simulations over one year, based on real meteorological data (average irradiance of 500 W/m², average annual wind speed of 5 m/s, temperatures between 2 and 27 °C), and carried out with Matlab/Simulink R2022a, have shown that the proposed model predictive control (MPC) strategy significantly improves the performance of DC micro-grids, particularly for heat pump applications. This strategy ensures a stable DC bus voltage (±1% around 500 V) and maintains the state of charge (SoC) of batteries between 40% and 78%, extending their service life by 20%. Compared with conventional methods, it improves energy efficiency by 15%, reduces operating costs by 30%, and cuts CO₂; emissions by 25%. By incorporating this control strategy, DC micro-grids offer a sustainable and reliable solution for heat pump applications, contributing to the transition towards a cleaner and more resilient energy system. This approach also opens new possibilities for renewable energy integration into power grids, providing intelligent and efficient energy management at the local level. Full article